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Like most approaches to the mind, cognitive science can be traced back to philosophical questions, especially about the nature of knowledge. For instance, Plato's dialog Meno, where he investigates the source of knowledge, can be seen as a foundational text.

The modern culture of cognitive science can be traced back to the early cyberneticists in the 1930s and 1940s, such as Warren McCulloch and Walter Pitts, who sought to understand the organizing principles of the mind . McCulloch and Pitts essentially invented the neural network, but did not have the computational tools to develop it into modern form.

In 1959, Noam Chomsky published a scathing review of B. F. Skinner's book Verbal Behavior. At the time, Skinner's behaviorist paradigm dominated psychology: Most psychologists focused on functional relations between stimulus and response, without positing internal representations. Chomsky's work showed that in order to explain language, we needed a theory like his generative grammar, which not only attributed internal representations but characterized their underlying order. This hugely successful theory would inspire much later cognitive science.

In the 1970s and early 1980s, much cognitive science research focused on the possibility of artificial intelligence. Researchers such as Marvin Minsky would write computer programs in languages such as LISP to attempt to formally characterize the steps that human beings went through, for instance, in making decisions and solving problems, in the hope of better understanding human thought, and also in the hope of creating artificial minds. This approach is known as "symbolic AI".

Eventually the limits of the symbolic AI research program became apparent. For instance, it seemed to be unrealistic to comprehensively list human knowledge in a form usable by a symbolic computer program. The late 80s and 90s saw the rise of neural networks and connectionism as a research paradigm.
Under this point of view, often attributed to James McClelland and David Rumelhart, the mind could be characterized as a set of complex associations, represented as a layered network. Critics argue that there are some phenomena which are better captured by symbolic models, and that connectionist models are often so complex as to have little explanatory power. But the flexibility and biological plausibility of these models made them very successful.

Today a plurality of approaches exist, from connectionism, to a focus on dynamical systems models, to attempts to reintroduce symbolic models using tools from modern computer science such as machine learning.

A central tenet of cognitive science is that a complete understanding of the mind/brain cannot be attained by studying only a single level. For example, consider the problem of remembering a phone number and recalling it later. How does this process occur? One approach would be to study behavior through direct observation. A person could be presented with a phone number, asked to recall it after some delay. Then the accuracy of the response could be measured. Another approach would be to study the firings of individual neurons while a person is trying to remember the phone number. Neither of these experiments on their own would fully explain how the process of remembering a phone number works. Even if the technology to map out every neuron in the brain in real-time were available, and it were known when each neuron was firing, it would still be impossible to know how a particular firing of neurons translates into the observed behavior. Thus an understanding of how these two levels relate to each other is needed. This can be provided by a functional level account of the process. Studying a particular phenomenon from multiple levels creates a better understanding of the processes that occur in the brain to give rise to a particular behavior.
Marr[5] gave a famous description of three levels of analysis:

the computational theory, specifying the goals of the computation;

representation and algorithm, giving a representation of the input and output and the algorithm which transforms one into the other; and

the hardware implementation, how algorithm and representation may be physically realized.

Many, but not all, who consider themselves cognitive scientists have a functionalist view of the mind—the view that mental states are classified functionally, such that any system that performs the proper function for some mental state is considered to be in that mental state. Thus, according to functionalism about the mind, even non-human systems, such as other animal species, alien life forms, or advanced computers can, in principle, have mental states. This perspective is one of the reasons the term "cognitive science" is not exactly coextensive with neuroscience, psychology, or some combination of the two.[How to reference and link to summary or text]

The term "cognitive" in "cognitive science" is "used for any kind of mental operation or structure that can be studied in precise terms" (Lakoff and Johnson, 1999). This conceptualization is very broad, and should not be confused with how "cognitive" is used in some traditions of analytic philosophy, where "cognitive" has to do only with formal rules and truth conditional semantics. (Nonetheless, that interpretation would bring one close to the historically dominant school of thought within cognitive science on the nature of cognition - that it is essentially symbolic, propositional, and logical.)

The earliest entries for the word "cognitive" in the OED take it to mean roughly pertaining "to the action or process of knowing". The first entry, from 1586, shows the word was at one time used in the context of discussions of Platonic theories of knowledge. Most in cognitive science, however, presumably do not believe their field is the study of anything as certain as the knowledge sought by Plato.

Cognitive science is a large field, and covers a wide array of topics on cognition. However, it should be recognized that cognitive science is not equally concerned with every topic that might bear on the nature and operation of the mind or intelligence. Social and cultural factors, emotion, consciousness, animal cognition, comparative and evolutionary approaches are frequently de-emphasized or excluded outright, often based on key philosophical conflicts. Another important mind-related subject that the cognitive sciences tend to avoid is the existence of qualia, with discussions over this issue being sometimes limited to only mentioning qualia as a philosophically-open matter. Some within the cognitive science community, however, consider these to be vital topics, and advocate the importance of investigating them.[6]

Below are some of the main topics that cognitive science is concerned with. This is not an exhaustive list, but is meant to cover the wide range of intelligent behaviors. See List of cognitive science topics for a list of various aspects of the field.

"... One major contribution of AI and cognitive science to psychology has been the information processing model of human thinking in which the metaphor of brain-as-computer is taken quite literally.
."AAAI Web pages.

Artificial intelligence (AI) involves the study of cognitive phenomena in machines. One of the practical goals of AI is to implement aspects of human intelligence in computers. Computers are also widely used as a tool with which to study cognitive phenomena. Computational modeling uses simulations to study how human intelligence may be structured. [7] (See the section on computational modeling in the Research Methods section.)

There is some debate in the field as to whether the mind is best viewed as a huge array of small but individually feeble elements (i.e. neurons), or as a collection of higher-level structures such as symbols, schemas, plans, and rules. The former view uses connectionism to study the mind, whereas the latter emphasizes symbolic computations. One way to view the issue is whether it is possible to accurately simulate a human brain on a computer without accurately simulating the neurons that make up the human brain.

Attention is the selection of important information. The human mind is bombarded with millions of stimuli and it must have a way of deciding which of this information to process. Attention is sometimes seen as a spotlight, meaning one can only shine the light on a particular set of information. Experiments that support this metaphor include the dichotic listening task (Cherry, 1957) and studies of inattentional blindness (Mack and Rock, 1998). In the dichotic listening task, subjects are bombarded with two different messages, one in each ear, and told to focus on only one of the messages. At the end of the experiment, when asked about the content of the unattended message, subjects cannot report it.

The ability to learn and understand language is an extremely complex process. Language is acquired within the first few years of life, and all humans under normal circumstances are able to acquire language proficiently. Some of the driving research questions in studying how the brain processes language include: (1) To what extent is linguistic knowledge innate or learned?, (2) Why is it more difficult for adults to acquire a second-language than it is for infants to acquire their first-language?, and (3) How are humans able to understand novel sentences?

The study of language processing ranges from the investigation of the sound patterns of speech to the meaning of words and whole sentences. Linguistics often divides language processing into orthography, phonology and phonetics, syntactics, semantics, and pragmatics. Many aspects of language can be studied from each of these components and from their interaction.

The study of language processing in cognitive science is closely tied to the field of linguistics. Linguistics was traditionally studied as a part of the humanities, including studies of history, art and literature. In the last fifty years or so, more and more researchers have studied knowledge and use of language as a cognitive phenomenon, the main problems being how knowledge of language can be acquired and used, and what precisely it consists of. Linguists have found that, while humans form sentences in ways apparently governed by very complex systems, they are remarkably unaware of the rules that govern their own speech. Thus linguists must resort to indirect methods to determine what those rules might be. If speech is indeed governed by rules, they appear to be opaque to any conscious consideration.

One approach to cognitive issues in language is the pragmatics of language, that is, the current use of the language by a real speaker. From a pragmatic analytical perspective it is possible to show that some people who have a profession in which they categorically work with language (e.g. journalists) have a behavior which is not predictable by known theories. The pragmatic approach is also useful in the study of collective distributed decision making, particularly in broadcasted systems (for instance aviation approach control - AAP).

Learning and development are the processes by which we acquire knowledge and information over time. Infants are born with little or no knowledge (depending on how knowledge is defined), yet they rapidly acquire the ability to use language, walk, and recognize people and objects. Research in learning and development aims to explain the mechanisms by which these processes might take place.

A major question in the study of cognitive development is the extent to which certain abilities are innate or learned. This is often framed in terms of the nature versus nurture debate. The nativist view emphasizes that certain features are innate to an organism and are determined by its genetic endowment. The empiricist view, on the other hand, emphasizes that certain abilities are learned from the environment. Although clearly both genetic and environmental input is needed for a child to develop normally, considerable debate remains about how genetic information might guide cognitive development. In the area of language acquisition, for example, some (such as Steven Pinker[8]) have argued that specific information containing universal grammatical rules must be contained in the genes, whereas others (such as Jeffrey Elman and colleagues in Rethinking Innateness) have argued that Pinker's claims are biologically unrealistic. They argue that genes determine the architecture of a learning system, but that specific "facts" about how grammar works can only be learned as a result of experience.

Memory allows us to store information for later retrieval. Memory is often thought of consisting of both a long-term and short-term store. Long-term memory allows us to store information over prolonged periods (days, weeks, years). We do not yet know the practical limit of long-term memory capacity. Short-term memory allows us to store information over short time scales (seconds or minutes).

Memory is also often grouped into declarative and procedural forms. Declarative memory--grouped into subsets of semantic and episodic forms of memory--refers to our memory for facts and specific knowledge, specific meanings, and specific experiences (e.g., Who was the first president of the U.S.A.?, or "What did I eat for breakfast four days ago?). Procedural memory allows us to remember actions and motor sequences (e.g. how to ride a bicycle) and is often dubbed implicit knowledge or memory .

Cognitive scientists study memory just as psychologists do, but tend to focus in more on how memory bears on cognitive processes, and the interrelationship between cognition and memory. One example of this could be, what mental processes does a person go through to retrieve a long-lost memory? Or, what differentiates between the cognitive process of recognition (seeing hints of something before remembering it, or memory in context) and recall (retrieving a memory, as in "fill-in-the-blank")?

Perception is the ability to take in information via the senses, and process it in some way. Vision and hearing are two dominant senses that allow us to perceive the environment. Some questions in the study of visual perception, for example, include: (1) How are we able to recognize objects?, (2) Why do we perceive a continuous visual environment, even though we only see small bits of it at any one time? One tool for studying visual perception is by looking at how people process optical illusions. The image on the right of a Necker cube is an example of a bistable percept, that is, the cube can be interpreted as being oriented in two different directions.

Action is taken to refer to the output of a system. In humans, this is accomplished through motor responses. Spatial planning and movement, speech production, and complex motor movements are all aspects of action.

In order to have a description of what constitutes intelligent behavior, one must study behavior itself. This type of research is closely tied to that in cognitive psychology and psychophysics. By measuring behavioral responses to different stimuli, one can understand something about how those stimuli are processed.

Reaction time. The time between the presentation of a stimulus and an appropriate response can indicate differences between two cognitive processes, and can indicate some things about their nature. For example, if in a search task the reaction times vary proportionally with the number of elements, then it is evident that this cognitive process of searching involves serial instead of parallel processing.

Psychophysical responses. Psychophysical experiments are an old psychological technique, which has been adopted by cognitive psychology. They typically involve making judgments of some physical property, e.g. the loudness of a sound. Correlation of subjective scales between individuals can show cognitive or sensory biases as compared to actual physical measurements. Some examples include:

sameness judgments for colors, tones, textures, etc.

threshold differences for colors, tones, textures, etc.

Eye tracking. This methodology is used to study a variety of cognitive processes, most notably visual perception and language processing. The fixation point of the eyes is linked to an individual's focus of attention. Thus, by monitoring eye movements, we can study what information is being processed at a given time. Eye tracking allows us to study cognitive processes on extremely short time scales. Eye movements reflect online decision making during a task, and they provide us with some insight into the ways in which those decisions may be processed.

Image of the human head with the brain. The arrow indicates the position of the hypothalamus.

Brain imaging involves analyzing activity within the brain while performing various cognitive tasks. This allows us to link behavior and brain function to help understand how information is processed. Different types of imaging techniques vary in their temporal (time-based) and spatial (location-based) resolution. Brain imaging is often used in cognitive neuroscience.

Single photon emission computed tomography and Positron emission tomography. SPECT and PET use radioactive isotopes, which are injected into the subject's bloodstream and taken up by the brain. By observing which areas of the brain take up the radioactive isotope, we can see which areas of the brain are more active than other areas. PET has similar spatial resolution to fMRI, but it has extremely poor temporal resolution.

Electroencephalography. EEG measures the electrical fields generated by large populations of neurons in the cortex by placing a series of electrodes on the scalp of the subject. This technique has an extremely high temporal resolution, but a relatively poor spatial resolution.

Functional magnetic resonance imaging. fMRI measures the relative amount of oxygenated blood flowing to different parts of the brain. More oxygenated blood in a particular region is assumed to correlate with an increase in neural activity in that part of the brain. This allows us to localize particular functions within different brain regions. fMRI has moderate spatial and temporal resolution.

Optical imaging. This technique uses infrared transmitters and receivers to measure the amount of light reflectance by blood near different areas of the brain. Since oxygenated and deoxygenated blood reflects light by different amounts, we can study which areas are more active (i.e., those that have more oxygenated blood). Optical imaging has moderate temporal resolution, but poor spatial resolution. It also has the advantage that it is extremely safe and can be used to study infants' brains.

Magnetoencephalography. MEG measures magnetic fields resulting from cortical activity. It is similar to EEG, except that it has improved spatial resolution since the magnetic fields it measures are not as blurred or attenuated by the scalp, meninges and so forth as the electrical activity measured in EEG is. MEG uses SQUID sensors to detect tiny magnetic fields.

Computational models require a mathematically and logically formal representation of a problem. Computer models are used in the simulation and experimental verification of different specific and general properties of intelligence. Computational modeling can help us to understand the functional organization of a particular cognitive phenomenon.
There are two basic approaches to cognitive modeling. The first is focused on abstract mental functions of an intelligent mind and operates using symbols, and the second, which follows the neural and associative properties of the human brain, and is called subsymbolic.

Symbolic modeling evolved from the computer science paradigms using the technologies of Knowledge-based systems, as well as a philosophical perspective, see for example "Good Old-Fashioned Artificial Intelligence" (GOFAI). They are developed by the first cognitive researchers and later used in information engineering for expert systems . Since the early 1990s it was generalized in systemics for the investigation of functional human-like intelligence models, such as personoids, and, in parallel, developed as the SOAR environment. Recently, especially in the context of cognitive decision making, symbolic cognitive modeling is extended to socio-cognitive approach including social and organization cognition interrelated with a sub-symbolic not conscious layer.

Subsymbolic modeling includes Connectionist/neural network models. Connectionism relies on the idea that the mind/brain is composed of simple nodes and that the power of the system comes primarily from the existence and manner of connections between the simple nodes. Neural nets are textbook implementations of this approach. Some critics of this approach feel that while these models approach biological reality as a repetition of how the system works, they lack explanatory powers because complicated systems of connections with even simple rules are extremely complex and often less interpretable than the system they model.

All the above approaches tend to be generalized to the form of integrated computational models of a synthetic/abstract intelligence, in order to be applied to the explanation and improvement of individual and social/organizational decision-making and reasoning.

Research methods borrowed directly from neuroscience and neuropsychology can also help us to understand aspects of intelligence. These methods allow us to understand how intelligent behavior is implemented in a physical system.

Cognitive science has much to its credit. Among other accomplishments, it has given rise to models of human cognitive bias and risk perception, and has been influential in the development of behavioral finance, part of economics. It has also given rise to a new theory of the philosophy of mathematics, and many theories of artificial intelligence, persuasion and coercion. It has made its presence firmly known in the philosophy of language and epistemology - a modern revival of rationalism - as well as constituting a substantial wing of modern linguistics. Fields of cognitive science have been influential in understanding the brain's particular functional systems (and functional deficits) ranging from speech production to auditory processing and visual perception. It has made progress in understanding how damage to particular areas of the brain affect cognition, and it has helped to uncover the root causes and results of specific disfunction, such as dyslexia, anopia, and hemispatial neglect.

The philosophical underpinnings of research in cognitive science have been criticized by philosophers and scientists alike[attribution needed]. In a speech given shortly before his death[9], B.F. Skinner stated that "cognitive science is the creationism of psychology."
See Functionalism (philosophy of mind) for an extended entry on criticisms.